Statistical models cannot agree well with the distribution of texture in a high-resolution synthetic aperture radar (SAR) image. Obtaining good results with traditional polarimetric SAR (PolSAR) image segmentation methods is therefore difficult. To overcome this problem

we propose a new PolSAR image segmentation method that combines KummerU distribution and the level set framework. The proposed method defines a new energy function with the KummerU probability density function as a high-resolution PolSAR image statistical model. The method is therefore suitable to PolSAR image segmentation. To implement PolSAR image segmentation

the parameters of KummerU distribution are estimated with the maximum likelihood method. The level set function is applied to the numerical solution of a partial differential equation. Experiments are based on synthetic-full-polarization and real-full-polarization SAR images. All experiments show good results

with an accuracy level above 95% compared with that of the traditional method and which therefore proves the applicability of the proposed algorithm. We propose a novel PolSAR image segmentation method based on a level set framework. The algorithm is applicable to high-resolution PolSAR images. Experiment results indicate that our model can be effectively used in most scenes and can separate targets from the background in homogeneous

heterogeneous

and extremely heterogeneous areas.